Variational quantum approximate support vector machine with inference transfer
Abstract A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic ru...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29495-y |
| _version_ | 1797865011010338816 |
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| author | Siheon Park Daniel K. Park June-Koo Kevin Rhee |
| author_facet | Siheon Park Daniel K. Park June-Koo Kevin Rhee |
| author_sort | Siheon Park |
| collection | DOAJ |
| description | Abstract A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability. |
| first_indexed | 2024-04-09T23:02:13Z |
| format | Article |
| id | doaj.art-db6bdd6e9da44f36920e9feb1a7cbcf6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:02:13Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-db6bdd6e9da44f36920e9feb1a7cbcf62023-03-22T10:58:02ZengNature PortfolioScientific Reports2045-23222023-02-0113111010.1038/s41598-023-29495-yVariational quantum approximate support vector machine with inference transferSiheon Park0Daniel K. Park1June-Koo Kevin Rhee2KAIST, School of Electrical EngineeringDepartment of Applied Statistics, Yonsei UniversityKAIST, School of Electrical EngineeringAbstract A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.https://doi.org/10.1038/s41598-023-29495-y |
| spellingShingle | Siheon Park Daniel K. Park June-Koo Kevin Rhee Variational quantum approximate support vector machine with inference transfer Scientific Reports |
| title | Variational quantum approximate support vector machine with inference transfer |
| title_full | Variational quantum approximate support vector machine with inference transfer |
| title_fullStr | Variational quantum approximate support vector machine with inference transfer |
| title_full_unstemmed | Variational quantum approximate support vector machine with inference transfer |
| title_short | Variational quantum approximate support vector machine with inference transfer |
| title_sort | variational quantum approximate support vector machine with inference transfer |
| url | https://doi.org/10.1038/s41598-023-29495-y |
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